Most years, the NHL trade deadline is basically the equivalent of an annual Y2K party: Much Ado About Nothing. The issue comes from the underlying inertia the permeates most of the league’s landscape.

The best players almost never switch teams in their prime (Seriously, who was the last top 10 player to leave their current team? Marian Hossa?)

Even when a trade does get made, there’s often no rhyme or reason to how it plays out. Sometimes you trade your team’s top disgruntled forward and get Seth Jones. Sometimes you get Adam Larsson.

So, to give the league’s decision makers a little kick in the butt, I’ve put together a trade model that identifies the trade value of every regular NHL player and determines what would be a fair return in a trade.

The first significant breakthrough in hockey analytics occurred in the mid-2000’s when analysts discovered the importance of Corsi in describing and predicting future success. Since that time, we’ve seen the creation of expected goals, WAR models, and more. Many have cited that the next big breakthrough in hockey analytics will come once the NHL is able to provide tracking data. We’ve already seen some of the incredible applications of the MLB’s Statcast data and the NBA’s SportVu data. Unfortunately, the NHL has no immediate plans to publicly provide this data and as such, many analysts have decided to manually obtain the data.

It’s the Olympics again, which means it’s time for everyone’s favorite activity: watching Canada underperform at ice-hockey! And while Hilary Knight breaking the hearts of Canadians is fun for everybody, the only thing that’s more fun is watching Hilary Knight break the hearts of Canadians while you have a statistical model that predicts each team’s likelihood of winning a medal! That’s right, Hockey Graphs is taking on the challenge of predicting the Women’s Olympic Hockey Tournament results.[1]

To state the obvious: goal-scoring is an essential skill for a hockey team. Players have made long careers by putting the puck in the net.

But how do players create goals? Skaters rely on all sorts of skills to score; some are fast, some have a huge shot, and some know how to be in the right place for an easy tap-in. But we don’t have a rigorous view of what those skills are, how they fit together, and which players rely on which ones.

In this piece, I take 100 of the top NHL goal-scorers and apply unsupervised learning techniques to group them into specific goal scoring types. The result is a classification that buckets the scorers into 5 categories: bombers, rushers, chance makers, chaos makers, and physical forces. These can help players understand how to apply their skill set to goalscoring. It can also help teams make sure that their system is putting their top players in a position to score.

Chris Watkins joined Adam Stringham to discuss some of his new work and Erik Karlsson’s recent comments. Is the NHL entering a new age of superstar transition? Will the leagues best players start jumping around in free agency? Any comments are appreciated, the goal is to produce a podcast that people want to hear. Please subscribe to the podcast on iTunes!

Hockey fans and analysts have always appreciated the importance of passing. But until the passing project led by Ryan Stimson, we couldn’t quantify that importance. His work supported by a team of volunteers and other analysts has established that the passing sequence prior to a shot is a significant predictor of the likelihood of the shot becoming a goal. His work also showed that measuring shots and shot assists combined as shot contributions is a better predictor of future performance for both players and teams than shots alone.

Knowing that, the logical next step is to use passing data in analysis whenever possible. Unfortunately, the NHL does not provide passing data so it must be manually tracked by people like Corey Sznajder. Corey’s work is invaluable and I encourage you to support him but he’s only one person.

This article attempts to estimate a player’s quantity of shot assists in a given sample using publicly available data to help fill in gaps where tracked data doesn’t exist.

Every once-in-a-while I will rant on the concepts and ideas behind what numbers suggest in a series called Behind the Numbers, as a tip of the hat to the website that brought me into hockey analytics: Behind the Net.

Hey! Remember me?

I work full-time for (slash help run)HockeyData, a data tracking and analysis company. Because of this conflict of interest, it limits what I can and cannot talk about. The good news is I can still talk generalities, the basics behind analytical thinking in hockey, and other peoples’ good work, which fits my Behind the Numbers series.

Why have there been so few updates then? Been busy (…lazy).

One generality I’d like to rant about is how we look at and evaluate statistics and models: how meaningful different numbers are and why we view them that way.Continue reading →

Hockey-Graphs is once again excited to be co-hosting the Vancouver Hockey Analytics Conference for its third year! We will be working with the Vancouver Canucks, Simon Fraser University and the great team at CanucksArmy.com.

This year we’ll have a 3 day conference, with 2 days of talks, tutorials, keynotes and great discussions. There will also be plenty of social events throughout the weekend.

All knowledge levels are welcome. If you are interested, you are more than welcome! Nobody will be turned away, everyone is encouraged to attend.

The Call for Presentations is currently open with a deadline of January 8th, 2018. See the website for more details or go here to submit your talk.

Registration has yet to open as we tabulate the final costs to host the venue, among other factors. Check back here or on Twitter, or add yourself to our mailing list, for more information on when it will open. (Note:Expect participants to be capped at around ~175 people.)